CN113879182A - Vehicle energy management control method, system, device and medium - Google Patents
Vehicle energy management control method, system, device and medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/60—Navigation input
- B60L2240/68—Traffic data
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/40—Application of hydrogen technology to transportation, e.g. using fuel cells
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- Engineering & Computer Science (AREA)
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- Sustainable Development (AREA)
- Sustainable Energy (AREA)
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- Transportation (AREA)
- Mechanical Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Fuel Cell (AREA)
Abstract
The invention discloses a vehicle energy management control method, a system, equipment and a medium. A vehicle energy management control method comprising: acquiring driving demand information input by a driver; selecting a feasible path; acquiring traffic state information on a feasible path; predicting to obtain a predicted running speed spectrum of the vehicle on the feasible path, and generating a prediction model; obtaining the optimal output power of the fuel cell in the feasible path, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path; controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading an actual operating speed spectrum and actual energy consumption data to a cloud end; acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud; and obtaining a corrected energy optimization model after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell. The invention also provides a vehicle energy management control system, equipment and a medium.
Description
Technical Field
The present invention relates to the field of vehicle management technologies, and in particular, to a method, a system, a device, and a medium for vehicle energy management control.
Background
The hydrogen energy is clean energy with high energy, no pollution and zero emission, has unique advantages in the industries of energy, transportation and the like, has short fuel supply time and high endurance mileage of a fuel cell vehicle, and becomes an excellent solution more and more in the field of commercial vehicle transportation by the characteristic, so that the hydrogen energy can completely replace the fuel vehicle.
The difficulty of the wide application of fuel cell vehicles lies in that the cost of the fuel cell and hydrogen is high, the improvement of the service life and the efficiency of the fuel cell is a key factor of the development of the fuel cell vehicle, and the energy management technology is the most effective and practical technical means at present under the condition that the fuel cell technology is difficult to have major breakthrough in a short time.
The energy management technology can effectively reduce the load change requirement of the driving working condition on the fuel cell and prolong the service life of the fuel cell; meanwhile, under the condition of meeting the requirement of a single running working condition, the instantaneous power of the working point of the fuel cell is reduced, the overall efficiency is improved, and the hydrogen consumption is reduced. Therefore, the use cost of the fuel cell automobile is reduced in the whole life cycle.
At present, for the application of energy management of fuel cells to vehicles, there are mainly rule-based control, fuzzy logic control and real-time optimization control.
Chinese patents (CN111791758A, CN 112092683A, CN 110843556B, CN110015192B) and other patents all perform energy management based on rules. Specific rules are formulated to control the start-stop and power point of the fuel cell by combining the real-time driving requirement of the vehicle, the temperature of the battery, the SOC and the power limit value of the fuel cell, so that the variable load requirement of the fuel cell is reduced, and the service life of the fuel cell is prolonged.
Chinese patent (CN112249001A) discloses a fuel cell vehicle energy management method based on fuzzy logic, which establishes a fuzzy logic model based on an initialization number set, and obtains the corresponding fuel cell power under the condition by combining parameters such as the real-time power demand of the vehicle, the battery SOC, the fuel cell current, and the efficiency.
Chinese patent (CN104002804B) discloses an energy control method for fuel cell hybrid power, which uses PMP optimal theory based on the characteristics of battery, fuel cell and DCDC to calculate the DCDC optimal power output in different states in advance, and obtains the vehicle power demand during driving to match the DCDC power output, thereby achieving the purpose of instantaneous optimization.
The control system and method disclosed in the above patent documents are optimized results obtained according to instantaneous driving requirements and characteristics of vehicles and parts thereof, and do not consider driver requirements, road traffic conditions and vehicles as a whole, and obtain an energy management scheme for realizing optimal vehicle life and energy consumption under the condition of meeting vehicle transportation requirements by combining actual transportation routes of drivers, efficiency requirements and road traffic condition factors obtained by an intelligent network connection technology.
Disclosure of Invention
Based on this, the present invention provides a vehicle energy management control method, system, device and medium for improving the efficiency and life of a fuel cell, considering the transportation requirements of a driver, acquiring road traffic state information through intelligent network connection, comprehensively considering the characteristics of a vehicle and a battery, and optimally managing the energy and the life of the fuel cell.
The first invention, the invention provides a vehicle energy management control method, comprising:
acquiring driving demand information input by a driver;
selecting a feasible path according to the driving demand information;
acquiring traffic state information on a feasible path;
according to the traffic state information, predicting to obtain a predicted operation vehicle speed spectrum of the vehicle on a feasible path, generating a prediction model, and uploading the predicted operation vehicle speed spectrum to a cloud end;
obtaining the optimal output power of the fuel cell in the feasible path according to a pre-stored energy optimization model, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path;
controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading an actual operating speed spectrum and actual energy consumption data to a cloud end;
acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud, and updating the prediction model;
and acquiring a corrected energy optimization model obtained after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell, and updating the energy optimization model.
In one embodiment, the driving demand information includes a transportation destination, and an expected arrival time and/or an expected passing route.
In one embodiment of the above technical solution, the traffic state information includes one or more of a speed of a traffic flow in a road section, a passing time, an intersection distance, and a traffic light time.
In one embodiment, the vehicle energy management control method further includes: extracting and classifying the vehicle speed spectrum characteristics of the predicted running vehicle speed spectrum;
comparing the vehicle speed spectrum characteristics with prestored vehicle speed characteristic spectrums in a prestored vehicle speed spectrum characteristic library, dividing vehicle speed spectrum types, and uploading the vehicle speed spectrum types to a cloud terminal;
and obtaining a result of comparing the vehicle speed spectrum characteristic with the prestored vehicle speed spectrum characteristic through cloud analysis, obtaining a new vehicle speed spectrum characteristic and updating the new vehicle speed spectrum characteristic into a prestored vehicle speed spectrum characteristic library.
In one embodiment, the extracting the vehicle speed spectrum feature of the predicted operating vehicle speed spectrum includes: extracting the vehicle speed spectrum characteristics of the predicted running vehicle speed spectrum by using cluster analysis;
the acquisition cloud analysis comparison vehicle speed spectrum characteristic and prestored vehicle speed spectrum characteristic results comprise: and acquiring a result of analyzing and comparing the vehicle speed spectrum characteristic with the prestored vehicle speed spectrum characteristic by the cloud through a genetic algorithm and/or a PMP algorithm.
In one embodiment of the foregoing technical solution, the actual energy consumption data includes consumption data corresponding to fuel cell power and/or motor power.
In one implementation, the technical scheme is in communication connection with the cloud terminal through the OTA.
In a second aspect, the present invention provides a vehicle energy management control system comprising:
a human-machine interaction module (HMI) configured to acquire driving demand information input by a driver;
the intelligent networking module is configured for selecting a feasible path according to the driving demand information;
acquiring traffic state information on the feasible path;
predicting to obtain a predicted operation vehicle speed spectrum of the vehicle on the feasible path according to the traffic state information, generating a prediction model, and uploading the predicted operation vehicle speed spectrum to a cloud end;
the vehicle-mounted real-time control module is configured and used for obtaining the optimal output power of the fuel cell in the feasible path according to a pre-stored energy optimization model, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path;
controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading an actual operating speed spectrum and actual energy consumption data to a cloud end;
acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud, and updating the prediction model;
and acquiring a corrected energy optimization model obtained after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell, and updating the energy optimization model.
In a third aspect, the present invention provides a vehicle energy management control apparatus, the apparatus comprising a storage device for storing one or more programs and a processor;
the processor, when the one or more programs are executed by the processor, implements a vehicle energy management control method as in any above.
In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one program, characterized in that when the program is executed by a processor, the vehicle energy management control method according to any one of the above is implemented.
Compared with the prior art, the vehicle transportation demand and the road traffic condition are considered globally in the energy management, and the energy management strategy has stronger pertinence to the actual operation condition; the optimization process requiring a large amount of calculation in energy optimization is executed on the cloud, the hardware requirement on the vehicle-mounted real-time control module is reduced, meanwhile, the database can be continuously updated into the vehicle-mounted real-time control module through the OTA function, and the energy management strategy and algorithm can be continuously corrected.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a block diagram of an exemplary flow of a vehicle energy management control method of the present invention.
FIG. 2 is an exemplary schematic of a vehicle energy management control method of the present invention.
Detailed Description
The terms of orientation of up, down, left, right, front, back, top, bottom, and the like, referred to or may be referred to in this specification, are defined relative to their configuration, and are relative concepts. Therefore, it may be changed according to different positions and different use states. Therefore, these and other directional terms should not be construed as limiting terms.
The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Please refer to fig. 1 and fig. 2. FIG. 1 is a block diagram of an exemplary flow of a vehicle energy management control method of the present invention. FIG. 2 is an exemplary schematic of a vehicle energy management control method of the present invention.
The first invention, the invention provides a vehicle energy management control method, comprising:
Specifically, the travel demand information includes a transportation destination, and a desired arrival time and/or a desired passing route.
In specific implementation, the driving demand information input by the driver can be acquired by adopting a human-computer interaction module HMI.
And 102, selecting a feasible path according to the driving demand information.
The feasible path can be automatically generated by using a navigation system in the prior art.
And 103, acquiring traffic state information on the feasible path.
Specifically, the traffic state information includes one or more of a road section traffic flow speed, a passing time, an intersection distance and a traffic light time.
In specific implementation, real-time acquisition of traffic state information can be realized by using the V2X equipment.
And 104, predicting to obtain a predicted operation vehicle speed spectrum of the vehicle on the feasible path according to the traffic state information, generating a prediction model, and uploading the predicted operation vehicle speed spectrum to a cloud.
And 105, obtaining the optimal output power of the fuel cell in the feasible path according to a pre-stored energy optimization model, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path.
And 106, controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading the actual operating speed spectrum and the actual energy consumption data to the cloud.
When the vehicle runs, the fuel cell outputs according to the optimal output power of the fuel cell, the motor responds to the operation of a driver, and the battery automatically matches the rest power.
In particular, the actual energy consumption data comprises consumption data corresponding to fuel cell power and/or motor power.
In addition, the charging and discharging power fed back by the SOC of the battery and the like can be uploaded to the cloud.
In addition, the actual running vehicle speed spectrum and the actual energy consumption data are uploaded to a cloud end and are also transmitted to a vehicle-mounted real-time control module of the vehicle for storage and data processing.
And 107, acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud, and updating the prediction model.
Preferably, the prediction model of the cloud can be updated to the vehicle-mounted real-time control module through the OTA periodically.
And step 108, acquiring a corrected energy optimization model obtained after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell, and updating the energy optimization model.
The initial energy optimization model prestores different characteristic working conditions and corresponding fuel cell optimal power output tables.
Preferably, the energy optimization model of the cloud can be updated to the vehicle-mounted real-time control module through the OTA periodically.
In addition, the optimal power output table of the fuel cell in the energy optimization model can be calculated and optimized off line.
Preferably, the vehicle energy management control method further includes: and 1041, extracting and classifying the vehicle speed spectrum characteristics of the predicted running vehicle speed spectrum. This step may be performed simultaneously with step 104.
Step 1042, comparing the vehicle speed spectrum characteristics with prestored vehicle speed characteristic spectrums in a prestored vehicle speed spectrum characteristic library, dividing vehicle speed spectrum types, and uploading the vehicle speed spectrum types to a cloud; this step may be performed simultaneously with step 1041.
And 1043, acquiring a result of comparing the vehicle speed spectrum characteristic with the prestored vehicle speed spectrum characteristic through cloud analysis, acquiring a new vehicle speed spectrum characteristic and updating the new vehicle speed spectrum characteristic into a prestored vehicle speed spectrum characteristic library. This step may be performed after step 1042.
Further, the extracting the vehicle speed spectrum feature of the predicted operation vehicle speed spectrum includes: and extracting the vehicle speed spectrum characteristics of the predicted running vehicle speed spectrum by using cluster analysis.
The acquisition cloud analysis comparison vehicle speed spectrum characteristic and prestored vehicle speed spectrum characteristic results comprise: and acquiring a result of analyzing and comparing the vehicle speed spectrum characteristic with the prestored vehicle speed spectrum characteristic by the cloud through a genetic algorithm and/or a PMP algorithm.
And (3) utilizing a genetic algorithm and/or PMP algorithm for analysis to ensure that the target service life of the fuel cell is taken as a constraint and searching the power output with minimum fuel consumption under the characteristic working condition.
Preferably, the communication connection is kept between the OTA and the cloud end, and data are updated to the vehicle-mounted real-time control module through the OTA (over-the-air technology) periodically.
It should be noted that the above-mentioned numbers are only an example for convenience of description and understanding, and the present application does not limit the timing sequence of each step.
In a second aspect, the present invention provides a vehicle energy management control system comprising:
the man-machine interaction module is configured for acquiring driving demand information input by a driver;
the intelligent networking module is configured for selecting a feasible path according to the driving demand information;
acquiring traffic state information on the feasible path;
predicting to obtain a predicted operation vehicle speed spectrum of the vehicle on the feasible path according to the traffic state information, generating a prediction model, and uploading the predicted operation vehicle speed spectrum to a cloud end;
the vehicle-mounted real-time control module is configured and used for obtaining the optimal output power of the fuel cell in the feasible path according to a pre-stored energy optimization model, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path;
controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading an actual operating speed spectrum and actual energy consumption data to a cloud end;
acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud, and updating the prediction model;
and acquiring a corrected energy optimization model obtained after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell, and updating the energy optimization model.
The man-machine interaction module supports a driver to input transportation destination information, expected arrival time and/or expected passing path and other driving requirement information; the intelligent networking module comprises navigation, positioning and V2X equipment and has the functions of acquiring real-time traffic information and recording the driving data of the vehicle; and the vehicle-mounted real-time control module (VCU) is used for analyzing and identifying the path characteristics and controlling the working point of the fuel cell in real time. A vehicle execution module can be further arranged and connected with the vehicle-mounted real-time control module, and the vehicle execution module comprises a whole vehicle and a power system component.
In a third aspect, the present invention provides a vehicle energy management control apparatus, the apparatus comprising a storage device for storing one or more programs and a processor;
when the one or more programs are executed by the processor, the processor implements a vehicle energy management control method as described.
The device may also preferably include a communication interface for communicating with external devices and for interactive transmission of data.
It should be noted that the memory may include a high-speed RAM memory, and may also include a nonvolatile memory (nonvolatile memory), such as at least one disk memory.
In a specific implementation, if the memory, the processor and the communication interface are integrated on a chip, the memory, the processor and the communication interface can complete mutual communication through the internal interface. If the memory, the processor and the communication interface are implemented independently, the memory, the processor and the communication interface may be connected to each other through a bus and perform communication with each other.
In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one program, characterized in that when the program is executed by a processor, the vehicle energy management control method as described is implemented.
It should be appreciated that the computer-readable storage medium is any data storage device that can store data or programs which can thereafter be read by a computer system. Examples of the computer readable storage medium include read-only memory, random-access memory, CD-ROMs, HDDs, DVDs, magnetic tapes, optical data storage devices, and the like. The computer readable storage medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
In some embodiments, the computer-readable storage medium may be non-transitory.
Compared with the prior art, the vehicle transportation demand and the road traffic condition are considered globally in the energy management, and the energy management strategy has stronger pertinence to the actual operation condition; the optimization process requiring a large amount of calculation in energy optimization is executed on the cloud, the hardware requirement on the vehicle-mounted real-time control module is reduced, meanwhile, the database can be continuously updated into the vehicle-mounted real-time control module through the OTA function, and the energy management strategy and algorithm can be continuously corrected.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. A vehicle energy management control method, characterized by comprising:
acquiring driving demand information input by a driver;
selecting a feasible path according to the driving demand information;
acquiring traffic state information on a feasible path;
according to the traffic state information, predicting to obtain a predicted operation vehicle speed spectrum of the vehicle on a feasible path, generating a prediction model, and uploading the predicted operation vehicle speed spectrum to a cloud end;
obtaining the optimal output power of the fuel cell in the feasible path according to a pre-stored energy optimization model, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path;
controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading an actual operating speed spectrum and actual energy consumption data to a cloud end;
acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud, and updating the prediction model;
and acquiring a corrected energy optimization model obtained after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell, and updating the energy optimization model.
2. The vehicle energy management control method according to claim 1, characterized in that the travel demand information includes a transportation destination, and a desired arrival time and/or a desired passing route.
3. The vehicle energy management control method of claim 1, wherein the traffic status information includes one or more of a road segment traffic speed, a transit time, an intersection distance, a traffic light time.
4. The vehicle energy management control method according to claim 1, characterized by further comprising: extracting and classifying the vehicle speed spectrum characteristics of the predicted running vehicle speed spectrum;
comparing the vehicle speed spectrum characteristics with prestored vehicle speed characteristic spectrums in a prestored vehicle speed spectrum characteristic library, dividing vehicle speed spectrum types, and uploading the vehicle speed spectrum types to a cloud terminal;
and obtaining a result of comparing the vehicle speed spectrum characteristic with the prestored vehicle speed spectrum characteristic through cloud analysis, obtaining a new vehicle speed spectrum characteristic and updating the new vehicle speed spectrum characteristic into a prestored vehicle speed spectrum characteristic library.
5. The vehicle energy management control method according to claim 4, wherein the extracting vehicle speed spectrum features of the predicted operating vehicle speed spectrum includes: extracting the vehicle speed spectrum characteristics of the predicted running vehicle speed spectrum by using cluster analysis;
the acquisition cloud analysis comparison vehicle speed spectrum characteristic and prestored vehicle speed spectrum characteristic results comprise: and acquiring a result of analyzing and comparing the vehicle speed spectrum characteristic with the prestored vehicle speed spectrum characteristic by the cloud through a genetic algorithm and/or a PMP algorithm.
6. The vehicle energy management control method according to claim 1, characterized in that the actual energy consumption data includes consumption data corresponding to fuel cell power and/or motor power.
7. The vehicle energy management control method of any of claims 1-6, wherein the communication connection is maintained with the cloud over the OTA.
8. A vehicle energy management control system, comprising:
the man-machine interaction module is configured for acquiring driving demand information input by a driver;
the intelligent networking module is configured for selecting a feasible path according to the driving demand information;
acquiring traffic state information on the feasible path;
predicting to obtain a predicted operation vehicle speed spectrum of the vehicle on the feasible path according to the traffic state information, generating a prediction model, and uploading the predicted operation vehicle speed spectrum to a cloud end;
the vehicle-mounted real-time control module is configured and used for obtaining the optimal output power of the fuel cell in the feasible path according to a pre-stored energy optimization model, and selecting the feasible path corresponding to the optimal output power of the fuel cell as the optimal path;
controlling the vehicle to operate according to the optimal output power of the fuel cell, and uploading an actual operating speed spectrum and actual energy consumption data to a cloud end;
acquiring a corrected prediction model obtained by comparing the predicted operation vehicle speed spectrum with the actual operation vehicle speed spectrum by the cloud, and updating the prediction model;
and acquiring a corrected energy optimization model obtained after the cloud compares the actual energy consumption data with the energy consumption data corresponding to the optimal output power of the fuel cell, and updating the energy optimization model.
9. A vehicle energy management control apparatus, comprising a storage device for storing one or more programs and a processor;
the processor, when the one or more programs are executed by the processor, implements the vehicle energy management control method of any of claims 1-7.
10. A computer-readable storage medium storing at least one program, characterized in that when the program is executed by a processor, it implements the vehicle energy management control method according to any one of claims 1 to 7.
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